ACM Computing Surveys (CSUR)
A tutorial on spectral clustering
Statistics and Computing
Cluster Identification in Nearest-Neighbor Graphs
ALT '07 Proceedings of the 18th international conference on Algorithmic Learning Theory
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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Finding clusters in data is a challenging task when the clusters differ widely in shapes, sizes, and densities. We present a novel spectral algorithm Speclus with a similarity measure based on modified mutual nearest neighbor graph. The resulting affinity matrix reflex the true structure of data. Its eigenvectors, that do not change their sign, are used for clustering data. The algorithm requires only one parameter --- a number of nearest neighbors, which can be quite easily established. Its performance on both artificial and real data sets is competitive to other solutions.